SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA€¦ · SOYBEAN CROPLAND MAPPING...

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SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA Mamta Kumari 1,* , C.S. Murthy 1 , Varun Pandey 1 , G.D. Bairagi 2 1 Agricultural Sciences & Applications Group, RS & GIS Applications Area, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India 2 Madhya Pradesh Council of Science and Technology, Bhopal, Madhya Pradesh, India *[email protected] Commission III, WG III/10 KEY WARDS: Soybean, Sentinel-1, SAR, Classification, Phenology, SVM ABSTRACT: Soybean, a high value oilseed crop, is predominantly grown in the rainfed agro-ecosystem of central and peninsular India. Accurate and up-to-date assessment of the spatial distribution of soybean cultivated area is a key information requirement of all stakeholders including policy makers, soybean farmers and consumers. A methodology for timely assessment with high precision of soybean crop using satellite data is yet not operational in India. In this scenario, synthetic aperture radar (SAR) has been shown to be a reliable form of gathering crop information, especially during monsoon season. In this work, repeat coverage from the C-band Sentinel-1 satellite over Ujjain district, Madhya Pradesh is used for in-season soybean crop mapping along with other agricultural land-cover types. The data were processed through four steps: (a) data preprocessing, (b) constructing smooth time series backscatter data, (c) soybean crop classification using knowledge-based decision rule classifier and support vector machines (SVM) and (d) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of soybean crop growing in the study region. Phenological characteristics were also derived from the smoothed S-1 VH backscatter time series to segregate early and late sown soybean. This information was used as an input to a decision-rule classifier and SVM in order to classify the input data into soybean and other crops. An overall accuracy of more than 80% using SVM and 75% using rule based classifier, in Ujjain district was achieved. These results demonstrate the scope for using time-series S-1 VH data to develop an operational soybean crop-monitoring framework. 1. INTRODUCTION Soybean (Glycine max), being a high value oilseed, is also known as the “Golden Bean” of the twentieth century. In the recent past, soybean cultivation has increased manifold as compared to any other oilseed crop in India and stands next only to groundnut. Soybean is primarily grown in the rainfed agro-ecosystem of central and peninsular India. About 53 per cent of the cropped area falls under this crop in Madhya Pradesh. In recent years, recurrent aberrant rainfall pattern and intermittent dry spell, both in time and space, were observed in these parts of country. These discrepancies inhibited the successful cultivation of soybean crop and leads to significant annual variation in cultivated area of soybean. Access to timely and reliable data on crop distribution and its condition assumes importance for enabling decision-makers to make informed decisions with respect to imports, exports, and other policy matters. Since, soybean is a short duration kharif season crops, mapping of soybean crops and their growth assessment using optical data is difficult due to consistent clouds. Mapping and monitoring of soybean crop is important due to various reasons such as, this crop is not covered in FASAL project; there is requirement from the Ministry of Agriculture to increase the number of crops for crop inventory; economic values of soybean crop are very high; and soybean crop is highly relevant from the crop insurance perspective. Optical remote sensing is one of the most commonly used data source for deriving crop information such as crop type map, biophysical parameters, crop growth stages, yield etc. Crop leaves are very sensitive to visible and infrared regions. However, persistent cloud cover during the monsoon season makes the availability of optical data difficult. This is almost impossible to obtain a cloud-free optical image during this period in a subtropical country like India. In this scenario, Synthetic Aperture Radar (SAR) is the only option to map the kharif season crops. Contrary to optical sensors, SAR sensors are capable to acquire images under all weather conditions which make it appropriate for crop mapping and monitoring during rainy season (Liao et al., 2018). However, the use of SAR data has not been established for agricultural applications, as compared to optical data. There are several reasons, such as, the complexity, cost and availability of SAR data, in addition to the difficulty of data interpretation. The recently launched Sentinel-1 with the advantage of free distribution provides more opportunities to derive crop type map. Being the high spatial and temporal resolution data, it allows not only to discriminate different crop types but also to assess their growth status. Previous studies performed the classification on Sentinel-1 data for land cover mapping by utilizing backscatter values of single dual polarimetric data (Abdikan et al. 2016, Whelen et al., 2017), especially to map rice crop (Son et al., 2018, Nguyen et al., 2017) and yielded maps with descent accuracy. However, no studies have been carried out to map soybean crop using Sentinel-1 data and very few studies are done using other SAR data (McNairn et al., 2014). In this study, potential of dual polarized Sentinel-1 backscatter data for in-season mapping of soybean crop is investigated. We analyzed multi-temporal C- band VV and VH polarized Sentinel-1 SAR data to evaluate the backscatter behavior of soybean crop and to map its spatial distribution in Ujjain district, Madhya Pradesh. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License. 109

Transcript of SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA€¦ · SOYBEAN CROPLAND MAPPING...

Page 1: SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA€¦ · SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA Mamta Kumari 1,*, C.S. Murthy 1, Varun Pandey 1,

SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA

Mamta Kumari 1,*, C.S. Murthy 1, Varun Pandey 1, G.D. Bairagi 2

1 Agricultural Sciences & Applications Group, RS & GIS – Applications Area, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India

2 Madhya Pradesh Council of Science and Technology, Bhopal, Madhya Pradesh, India

*[email protected]

Commission III, WG III/10

KEY WARDS: Soybean, Sentinel-1, SAR, Classification, Phenology, SVM

ABSTRACT:

Soybean, a high value oilseed crop, is predominantly grown in the rainfed agro-ecosystem of central and peninsular India. Accurate

and up-to-date assessment of the spatial distribution of soybean cultivated area is a key information requirement of all stakeholders

including policy makers, soybean farmers and consumers. A methodology for timely assessment with high precision of soybean crop

using satellite data is yet not operational in India. In this scenario, synthetic aperture radar (SAR) has been shown to be a reliable

form of gathering crop information, especially during monsoon season. In this work, repeat coverage from the C-band Sentinel-1

satellite over Ujjain district, Madhya Pradesh is used for in-season soybean crop mapping along with other agricultural land-cover

types. The data were processed through four steps: (a) data preprocessing, (b) constructing smooth time series backscatter data, (c)

soybean crop classification using knowledge-based decision rule classifier and support vector machines (SVM) and (d) accuracy

assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of soybean crop

growing in the study region. Phenological characteristics were also derived from the smoothed S-1 VH backscatter time series to

segregate early and late sown soybean. This information was used as an input to a decision-rule classifier and SVM in order to

classify the input data into soybean and other crops. An overall accuracy of more than 80% using SVM and 75% using rule based

classifier, in Ujjain district was achieved. These results demonstrate the scope for using time-series S-1 VH data to develop an

operational soybean crop-monitoring framework.

1. INTRODUCTION

Soybean (Glycine max), being a high value oilseed, is also

known as the “Golden Bean” of the twentieth century. In the

recent past, soybean cultivation has increased manifold as

compared to any other oilseed crop in India and stands next

only to groundnut. Soybean is primarily grown in the rainfed

agro-ecosystem of central and peninsular India. About 53 per

cent of the cropped area falls under this crop in Madhya

Pradesh. In recent years, recurrent aberrant rainfall pattern and

intermittent dry spell, both in time and space, were observed in

these parts of country. These discrepancies inhibited the

successful cultivation of soybean crop and leads to significant

annual variation in cultivated area of soybean. Access to timely

and reliable data on crop distribution and its condition assumes

importance for enabling decision-makers to make informed

decisions with respect to imports, exports, and other policy

matters. Since, soybean is a short duration kharif season crops,

mapping of soybean crops and their growth assessment using

optical data is difficult due to consistent clouds. Mapping and

monitoring of soybean crop is important due to various reasons

such as, this crop is not covered in FASAL project; there is

requirement from the Ministry of Agriculture to increase the

number of crops for crop inventory; economic values of

soybean crop are very high; and soybean crop is highly relevant

from the crop insurance perspective.

Optical remote sensing is one of the most commonly used data

source for deriving crop information such as crop type map,

biophysical parameters, crop growth stages, yield etc. Crop

leaves are very sensitive to visible and infrared regions.

However, persistent cloud cover during the monsoon season

makes the availability of optical data difficult. This is almost

impossible to obtain a cloud-free optical image during this

period in a subtropical country like India. In this scenario,

Synthetic Aperture Radar (SAR) is the only option to map the

kharif season crops. Contrary to optical sensors, SAR sensors

are capable to acquire images under all weather conditions

which make it appropriate for crop mapping and monitoring

during rainy season (Liao et al., 2018). However, the use of

SAR data has not been established for agricultural applications,

as compared to optical data. There are several reasons, such as,

the complexity, cost and availability of SAR data, in addition to

the difficulty of data interpretation. The recently launched

Sentinel-1 with the advantage of free distribution provides more

opportunities to derive crop type map. Being the high spatial

and temporal resolution data, it allows not only to discriminate

different crop types but also to assess their growth status.

Previous studies performed the classification on Sentinel-1 data

for land cover mapping by utilizing backscatter values of single

dual polarimetric data (Abdikan et al. 2016, Whelen et al.,

2017), especially to map rice crop (Son et al., 2018, Nguyen et

al., 2017) and yielded maps with descent accuracy. However,

no studies have been carried out to map soybean crop using

Sentinel-1 data and very few studies are done using other SAR

data (McNairn et al., 2014). In this study, potential of dual

polarized Sentinel-1 backscatter data for in-season mapping of

soybean crop is investigated. We analyzed multi-temporal C-

band VV and VH polarized Sentinel-1 SAR data to evaluate the

backscatter behavior of soybean crop and to map its spatial

distribution in Ujjain district, Madhya Pradesh.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License.

109

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2. METHODOLOGY

2.1 Study area

Ujjain district is situated in the heart of Malwa Plateau, Madhya

Pradesh at a general elevation of 527 meter above mean sea

level. The district area extends between the parallels of latitude

220 50' and 230 46' North and between the meridians of

longitude 750 08' and 760 16' East. The normal annual rainfall of

Ujjain district is 914.5 mm. Ujjain district receive maximum

rainfall (about 92.1%) during southwest monsoon period i.e.

June to November. The total geographical area of the district is

6,130.23 Sq. Km and divided in seven tehsil and six blocks. The

irrigation facilities in Ujjain district are moderate and

groundwater is the main source of irrigation in the district.

Ujjain district is mainly agriculture-based district and its

cropping pattern is diversified. Black cotton soils with heavy to

light texture are found in the whole area. Soybean is the most

dominant kharif season crop. Other crops grown in kharif

season are urad, maize, vegetables etc.

Fig 1. Study area: Ujjain district

2.2 Data collection

The input datasets for the analysis are listed hereunder;

2.2.1. Sentinel 1A data: This study used dual-polarized

(VV/VH) C-band imagery from the European Space Agency’s

(ESA) Sentinel-1A, covering 2017 soybean crop growing

season. The Sentinel-1A operates at a radar wavelength of about

5.6 cm (corresponding to frequency 5.405 GHz; C-band), with a

repeat coverage of 12 days. It has four imaging modes:

interferometric wide swath (IW), extra wide swath (EW), strip

map (SM), and wave (WV). This study used the

S1A_IW_GRDH product, which contains VH and VV

polarizations with a swath width of approximately 250 km and a

spatial resolution of 10 m; which provides backscatter

normalized with respect to area. Eleven dates (22 nos. images)

with a descending orbit during June to October 2017 were used

in the analysis. The incidence angle of the VH and VV images

acquired over the study region ranges from approximately 37–

45°. The details of the data collection dates are:01st June, 13th

June, 25th June, 19th July, 31st Jul, 12th Aug, 24th Aug, 05th Sep,

17th Sep, 29th Sep, 23rd Oct.

2.2.2 Optical data and Ancillary data: For the study area,

multi-temporal Resourcesat-2 AWiFS15-days NDVI composite

and Landsat-8 OLI data for the year 2017 (June to October)

were used to produce reference classification maps for

validating the Sentinel-1A soybean crop map. The Resourcesat

2 AWiFS15-days NDVI composite data of 56 m resolution was

obtained from NRSC. The Landsat 8 OLI data of 30 m

resolution was downloaded from the USGS website.

2.2.3 Ground reference data: Field information on the

distribution of different land cover classes in general and

soybean crop in particular, has been collected. A mobile

application - FASAL app –developed by NRSC was used

during field visit during September 2017. Various information

such as crop type, crop sowing and harvesting dates, field

moisture status, plant density etc., were collected during field

visit, using mobile app. This aided in faster and more efficient

collection of ground information about the crop along with field

photographs, as well as in building up a geodatabase with which

could be directly uploaded on to Bhuvan geoportal.

2.3 Satellite data processing

SAR derived time-series backscatter data are the key datasets in

this project as per the methodology framework depicted in

Figure 2.

2.3.1 Data pre-processing: The Sentinel-1A data were pre-

processed using ESA’s Sentinel’s Application Platform

(SNAP). Firstly, radiometric calibration was performed to

convert digital pixel values of VV and VH amplitude into sigma

naught (σ°) values that can be directly related to the radar

backscatter of the scene. Radiometric calibration is required to

remove the errors due to fluctuations in the transmitted power,

receiver gains, system noise and the illumination pattern of the

antenna. Speckles from the σ° data were filtered using adaptive

Lee filter with 5 × 5 moving window size to make the

interpretation of features easy. The terrain correction was

applied to compensate the distortions due to the side-looking

geometry of images. In this study, the Range Doppler Terrain

correction algorithm that uses the elevation data from the 1 arc-

second DEM product from the Shuttle Radar Topography

Mission (SRTM) was used for geocoding and terrain correction.

In this process, data are resampled and reprojected to the

Universal Transverse Mercator (UTM) coordinate system (zone

43 N) with a grid of 10 m spacing maintaining the 20 m spatial

resolution. The data were then mosaicked and subset over the

study region. Because Sentinel-1 data are not fully polarimetric,

polarimetric decomposition methods were not available to be

included in the analysis. Both polarizations were examined

separately as well as in combination.

Fig 2. Framework of methodology showing steps for soybean

crop classification in study area

2.3.2 Time series filtering to construct smooth time

series backscatter data: The backscatter time-series data

Data collection

• Multi-temporal Sentinel-1A

data (June to September,

2017)

• Single date Landsat-8 OLI

data (Mosaic of 20th-29th

September, 2017)

• Ground data (2017) using

mobile app

Data Pre-processing

• Radiometric correction

• Speckle noise filtering

• Terrain correction and projection

• Image mosaicking and layer stacking

Constructing smooth time series VH

data

• Temporal linear interpolation

• Noise filtering with wavelet transform

Soybean crop classification

• Training data selection/Rule formation

• Support Vector machine/ Rule based

classification

Accuracy assessment

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License.

110

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possess the short-term influence of environmental conditions

and inherent noises in the Sentinel-1A data (such as noise due to

speckle). Filtering is required to reduce these noises that can

potentially affect the classification results. In current study,

wavelet transform, an adaptive noise reduction method, is used

to cope up the problem. It has certain advantages such as to

preserve the full non-linearity and non-stationarity in physical

space, which is more conducive to data denoising and

information extraction. It has been extensively used for the

denoising the time series of satellite data (Sakamoto et al. 2005;

Galford et al. 2008; Martinez & Gilabert 2009; Chen, Huang et

al. 2011; Chen, Son et al. 2011). The wavelet transform W(s, τ)

of a signal x (t) can be expressed as follows:

𝑊(𝑠, 𝜏) = ∫𝑥(𝑡)𝜓 (𝑡 − 𝜏

𝑠)𝑑(𝑡),

CZ: Calibration zones, SP: Seasonality parameters, AS:

Adaptation strength

Where s > 0 and τ ϵ R; x (t) is the analyzed input signal; ψ (t) is

the mother wavelet; and sand 𝜏are scaling and translation

parameters. A mother wavelet, Coiflet function (with order 4)

was preferred over Daubechies and Symlet functions for signal

decomposition, to get time profile of backscatter data for

deriving the crop phenological information (Sakamoto et al.

2005). The processed output is a smoothed backscatter signal,

which will be used for the extraction of different phenological

stages of soybean (e.g., a start of the season, heading time, and

length of season); in order to discriminate the soybean pixels

from the other land cover classes.

2.3.3 Extraction of Vegetation Phenology Parameters:

The three phenological indicators, viz., date of beginning of

season (DoS), date of maximum backscatter (DoM), and Length

of the season (LoS) are derived in this step. This allows

delineating the areas as “early planted soybean and late planted

soybean”, and all other areas were placed in a generalized “non-

soybean” class. These parameters are summarized in Table 1.

Table1. Phenological parameters for the rule-based

classification

2.3.4 Training data selection: This study focused on

delineating soybean crops (early and late planted) in Ujjain

district. The non-cropped areas, including orchards, other crops

(maize, vegetables), forests, built-up areas and water bodies,

were masked out using the smooth VH backscatter data to limit

the analysis to the cropped areas. The Support vector machine

(SVM) used training samples extracted from the filtered time

series VH backscatter data. The fuzzy C-mean clustering

algorithm (Dunn 1973; Bezdek 1981) was applied to classify

the extracted pixels into several clusters. The clusters reflecting

the pattern of the soybean crop being analyzed were selected to

train SVM for classification.

2.3.5 Support vector machines: Support Vector Machines

(SVMs), a non-parametric statistical learning method, has

recently been used in image classification for various

applications. Being a supervised learning method, the SVM

need user-defined parameters and each parameter has different

impact on kernels hence the classification accuracy of SVMs is

based upon the choice of the parameters and kernels. The

algorithm projects training data of the input space into a high-

dimensional space using a kernel function in which the classes

can be separable.

The basic idea behind SVMs is to separate the two different

classes through a hyperplane which is specified by its normal

vector ‘w’ determining the hyperplane orientation and the bias

‘b’. The optimal hyperplane can be given as⟨𝑤,⃗⃗⃗⃗ 𝑥 ⟩ + 𝑏 = 0 and

the corresponding decision function is𝑓(𝑥 ) = 𝑠𝑔𝑛(⟨�⃗⃗� , 𝑥⃗⃗ ⃗⟩ + 𝑏),

where x is a point lying on the hyperplane. The sign of f(x)

depends on the side of the hyperplane where the sample lies.

The norm of the normal vector was equal to the inverse of the

distance, of the closest sample of both classes to the hyperplane.

Hence, the optimal separating hyperplane with maximal margin

can be formulated as the quadratic optimization problem (min τ

(�⃗⃗� ) =1

2|�⃗⃗� |2) and solved using Karush Kuhn Tucker conditions

and Lagrange multipliers (Arfken 1985; Sundaram 1996).

For a non-linear case, a kernel function 𝐾(𝑥 , �⃗⃗� ) is introduced

to enable an efficient computation. The ‘kernel trick’ can be

applied since all feature vectors only occurred in dot products.

In current study, radial basis function (RBF) was used for SVM

classification of the VH backscatter data as it has demonstrated

to produce more accurate results than other functions such as

linear and polynomial kernels (Camps-Valls and Bruzzone

2005) for classification of remotely sensed data (Huang et al.

2002; Keerthi and Lin 2003; Pal and Mather 2005). The

parameters such as cost parameter (C), gamma (γ), bias term (r)

were used in SVM classification. The optimal values of these

parameters were identified to increase the classification

accuracy, by various permutations and combinations of these

parameters in different models for sensitivity analysis of SVM

architecture.

2.3.6 Accuracy Assessment: For validation and evaluation

of classification results, standard accuracy assessment measures

i.e., kappa coefficient, overall accuracy, omission error, and

commission error were used.

3. RESULTS & DISCUSSIONS

The current study attempts to delineate kharif soybean crop area

using SAR data. The 12-day time series VH backscatter from

Sentinel-1A SAR has been effectively used to delineate the

soybean crop area from other land use land cover types, in view

of soybean being short duration crop and grown in the during

monsoon season. The present study was carried out in Ujjain

district. Madhya Pradesh. The results obtained in this study are

described in this section.

3.1 Temporal soybean backscatter signature from

Sentinel-1 SAR Data

Figure 3 shows the colour composites, which are created by

using the multi-temporal SAR acquisitions in order to highlight

the temporal characteristics within the soybean fields. The red,

blue, and green coolers in the figure# correspond to the images

acquired during the sowing (June/July), Flowering/Pod

formation (August/September) and post harvested (October)

period, respectively.

Parameters Definition and Explanation

DoS During the crop growing season, the date of the beginning of

season is defined as the first localminima in time-series of

smooth VH backscatter data

DoM During the growing season, the date when backscatter reaches

a maximum value Isdefined as the local maxima in time-series

of smooth VH backscatter data.

This date must come after the date of the beginning of season,

where it reaches its local minimum backscatter value.

LoS The length of the season is defined as the number of days

difference between DoM and DoS.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License.

111

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Fig 3. False colour composite of temporal SAR image

The profiles produced from the wavelet transform of VH

backscatter data revealed temporal discriminations between two

types of soybean crop (early sown and late sown) and those of

other land use cover types (e.g. orchards, water body and built-

up area). The VH backscatter intensities of soybean crops

showed a gradual increase of backscatter values after sowing on

reaching the maximum value during the pod formation of crops.

The backscatter intensities of soybean crops during the sowing

period were approximately −21 dB and those during the pod

formation periods (i.e. approximately 50 days after sowing)

were −15 dB, respectively. After sowing period, soybean crop

height increase with the unfolding of trifoliate leaves till the

start of flowering stage. Thus, the backscatter intensity showed

a significant increase during this vegetative phase due to the

greater the contribution of VH from volume scattering of

soybean plant’s internal canopies. After pod formation period,

the soybean plant was characterized by a decrease in the number

of leaves (i.e. ripening stages), the backscatter intensities

slightly decreased until the harvesting period. The VH

backscatter intensities from orchards and built-up areas were

relatively high. The backscatter values of water were also

distinguishable from those of soybean crop and other land use

cover types, showing very low VH backscatter values

throughout the year. Only VH backscatter temporal profile

shown consistent temporal pattern and shown an increase in

backscatter with crop growth, however, VV backscatter and

derivatives of backscatter reflected the inconsistent and abrupt

changes with crop growth in temporal backscatter profile.

3.2 Characteristics of smooth VH backscatter LUC

profiles

Fig 4 illustrated the some noise in raw VH, VV, VH/VV ratio

and VH+VV sum backscatter profiles in the signature of

soybean crop growth profile. Among all these backscatter

profiles, VH backscatter profile showed maximum potential by

resembling the growth profile of soybean. Therefore, only VH

backscatter profile was chosen for further analysis; to reduce the

computation time and complexity. The smooth curves (for VH

backscatter) obtained from the wavelet transform indicated that

the noise was significantly mitigated, as shown in Fig 5. The

cross-polarized VH backscatter was more sensitive to the

growth of soybean crop than the like-polarized VV backscatter,

characterizing the temporal variations of the soybean crop

during the season. The more sensitivity of the soybean in the

cross-polarized VH backscatter was mainly attributed to the

depolarization of signal that occurred within the soybean

canopy during the multiple reflection of the incoming signal

inside the vegetation volume.

.

Fig 4. Temporal backscatter of Sentinel-1A data (a) VH (b) VV

(c) VH/VV ratio and (d) VH+VV sum

Fig 5. Original and Smoothed temporal profile of backscatter

from soybean crop from different places

3.3 Thresholds Selection for rule based classification

The temporal backscatter signatures is shown in Figure 6 served

to define specific thresholds which were applied to the Sentinel-

1 time series in order to generate soybean crop area maps for the

growing season of 2017.

To map the different type of soybean crop areas based on

sowing time, the first two key parameters, DoS- backscatter at

the beginning of season and DoM- the peak (maximum) of VH

backscatter are very important. The amplitude backscatter

difference (Δσ; the difference between DoS and DoM) also

plays a significant role to decide the threshold. The

corresponding positions (date) and backscatter values of DoS

showed in the Figure 8.0. The peak and valley of VH

backscatter (i.e., DoS, DoM) within the one soybean-growing

cycle can be identified by local maxima. To eliminate

unrealistic peaks, a threshold for VH backscatter is required.

These two parameter thresholds (i.e., DoM ≥ 80 and Δσ ≥ 30)

were selected based on a conservative deduction from training

data. The third parameter, length of the season (LoS) which is

defined as the temporal distance(number of days difference)

between the date of beginning of season and the date when

maximum backscatter value is recorded. This temporal distance

has to be greater than the shortest possible soybean growing

cycle and smaller than the longest possible soybean growing

(a)

(d)

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-12

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-10

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-8

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VH

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dB

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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License.

112

Page 5: SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA€¦ · SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA Mamta Kumari 1,*, C.S. Murthy 1, Varun Pandey 1,

cycle. From crop calendar and expert knowledge about soybean

growing season in the study areas, the threshold of temporal

distance which is about 70 and 110 days, respectively. If all

three conditions are met then the pixel is classified as any type

of soybean, otherwise as non-soybean area. To discriminate

within soybean crop types, DoS and harvesting time window

was identified to decide whether it is early sown or late sown

soybean crop.

Fig 6. Smoothed temporal VH backscatter profile from late and

early sown soybean

3.4 Comparison of Sentinel-1A derived spatial

distribution of soybean crop and classification accuracies

The mapping results obtained from the classification of the

smooth time series Sentinel-1A VH backscatter data using rule

based and SVM indicated comparable spatial distributions of

soybean crop (both early and late planted) with those from the

ground reference data. In study area, soybean crop is extensively

grown in kharif season and occupied most of the study region,

with considerable variability in sowing time. The classification

maps produced from rule based and SVM (Fig 7) were

evaluated with the ground reference data (200 points) to assess

the accuracy of the mapping methods. The optimal values of

SVM parameters were identified (Kernel= RBF, γ =0.2, C =100,

Pyramid level=0.2, Classification probability = 0.5) for soybean

crop classification in study area. The results indicated that the

mapping results obtained from SVM was slightly better that

those from rule based. The overall accuracy and Kappa

coefficient achieved from SVM were respectively 83.1% and

0.74, while the values from rule based were 76.3% and 0.68,

respectively (Table 2). There were some error sources

exaggerating the mapping results, possibly attributed to the

small variation of incidence angle, ground reference error and

mixed-pixel issues. We could see that soybean fields, relatively

small and scattered, were mixed with other land use cover types,

such as other crops (urad) having same seasonal pattern,

orchards, canals and rivers in settlement areas. The mixed-pixel

and boundary effects could complicate the radar backscattering

coefficients, causing temporal uncertainty in discriminating

between signatures of soybean crops and other land-use types,

consequently leading to misclassification of soybean crop and

exaggerating mapping errors in these areas. Moreover, the 12-

day temporal resolution of Sentinel-1A is relatively long to

capture phenological information of soybean crop (e.g. sowing

and maximum growth periods), which might lead to an

increased uncertainty in soybean crop type classification,

especially areas of high variability in sowing. These results were

reaffirmed by the MP government’s soybean crop area statistics

of Ujjain district with the relative error in area values of 16%

and 8.5% for rule based and SVM, respectively.

Fig 7. Crop map showing spatial distribution of soybean along

with other land use cover type derived by (a) SVM (b) Rule

based classifier

Table 2. Accuracy assessment

4. CONCLUSIONS

The current study investigated the feasibility of multi-temporal

Sentinel-1A VH backscatter data for soybean crop classification

in Ujjain district, Madhya Pradesh. The results indicated that the

smooth VH backscatter profiles (wavelet transform-based)

could represent the temporal variations in growth profile of

soybean crop in the study region. Moreover, the smooth VH

backscatter time series could also segregate the early and late

sown soybean. As the mixed-pixel, sowing time variability and

crop with similar phenology could lower the level of

classification accuracy, this study demonstrated that rule based

and SVM classifier confirmed the validity for mapping soybean

in the region. The overall accuracy and Kappa coefficient

achieved from rule were, respectively, 76.3% and 0.68, while

the values from SVM were 83.1% and 0.74, respectively. These

results demonstrate the scope for using time-series Sentinel-1

VH data to develop an operational large-scale soybean crop

mapping framework.

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Year Soybean area (MP

state agriculture

portal) (lakh ha)

Sentinel-1 derived soybean area (lakh ha)

by SVM

classifier

by Rule based

classifier

2014-15 4.33 -

2015-16 4.55

2017-18 4.82 5.15

Overall accuracy (%) (Kappa

statistic)

83.1 (0.74) 76.3 (0.68)

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License.

113

Page 6: SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA€¦ · SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA Mamta Kumari 1,*, C.S. Murthy 1, Varun Pandey 1,

There is further scope of improving the classification accuracy

through complementary use of cloud free optical data (such as

Sentinel-2) in combination with SAR data.

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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019 | © Authors 2019. CC BY 4.0 License.

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